Szczegóły publikacji
Opis bibliograficzny
A new short term electrical load forecasting by type-2 fuzzy neural networks / Man-Wen Tian, Khalid Alattas, Fayez El-Sousy, Abdullah Alanazi, Ardashir Mohammadzadeh, Jafar Tavoosi, Saleh Mobayen, Paweł SKRUCH // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073. — 2022 — vol. 15 iss. 9 art. no. 3034, s. 1–14. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 13–14, Abstr. — Publikacja dostępna online od: 2022-04-21
Autorzy (8)
- Tian Man-Wen
- Alattas Khalid A.
- El-Sousy Fayez F. M.
- Alanazi Abdullah
- Mohammadzadeh Ardashir
- Tavoosi Jafar
- Mobayen Saleh
- AGHSkruch Paweł
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 140107 |
|---|---|
| Data dodania do BaDAP | 2022-05-11 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en15093034 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
In this study, we present a new approach for load forecasting (LF) using a recurrent fuzzy neural network (RFNN) for Kermanshah City. Imagine if there is a need for electricity in a region in the coming years, we will have to build a power plant or reinforce transmission lines, so this will be resolved if accurate forecasts are made at the right time. Furthermore, suppose that by building distributed generation plants, and predicting future consumption, we can conclude that production will be more than consumption, so we will seek to export energy to other countries and make decisions on this. In this paper, a novel combination of neural networks (NNs) and type-2 fuzzy systems (T2FSs) is used for load forecasting. Adding feedback to the fuzzy neural network can also benefit from past moments. This feedback structure is called a recurrent fuzzy neural network. In this paper, Kermanshah urban electrical load data is used. The simulation results prove the efficiency of this method for forecasting the electrical load. We found that we can accurately predict the electrical load of the city for the next day with 98% accuracy. The accuracy index is the evaluation of mean absolute percentage error (MAPE). The main contributions are: (1) Introducing a new fuzzy neural network. (2) Improving and increasing the accuracy of forecasting using the proposed fuzzy neural network. (3) Taking data from a specific area (Kermanshah City) and forecasting the electrical load for that area. (4) The ability to enter new data without calculations from the beginning.
